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Directed Ray Distance Functions for 3D Scene Reconstruction

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

We present an approach for full 3D scene reconstruction from a single unseen image. We trained on dataset of realistic non-watertight scans of scenes. Our approach uses a predicted distance function, since these have shown promise in handling complex topologies and large spaces. We identify and analyze two key challenges for predicting such image conditioned distance functions that have prevented their success on real 3D scene data. First, we show that predicting a conventional scene distance from an image requires reasoning over a large receptive field. Second, we analytically show that the optimal output of the network trained to predict these distance functions does not obey all the distance function properties. We propose an alternate distance function, the Directed Ray Distance Function (DRDF), that tackles both challenges. We show that a deep network trained to predict DRDFs outperforms all other methods quantitatively and qualitatively on 3D reconstruction from single image on Matterport3D, 3DFront, and ScanNet. (Project Page: https://nileshkulkarni.github.io/scene_drdf)

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Acknowledgement

We would like the thank Alexandar Raistrick and Chris Rockwell for their help with the 3DFront dataset. We like to thank Shubham Tulsiani, Ekdeep Singh Lubana, Richard Higgins, Sarah Jabour, Shengyi Qian, Linyi Jin, Karan Desai, Mohammed El Banani, Chris Rockwell, Alexandar Raistrick, Dandan Shan, Andrew Owens for comments on the draft versions of this paper. NK was supported by TRI. Toyota Research Institute (“TRI”) provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity

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Kulkarni, N., Johnson, J., Fouhey, D.F. (2022). Directed Ray Distance Functions for 3D Scene Reconstruction. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13662. Springer, Cham. https://doi.org/10.1007/978-3-031-20086-1_12

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